He did postdoctoral work 14-22, An Efficient Learning Procedure for Deep Boltzmann Machines, Neural Computation, vol. 1063-1088, Energy-Based Models for Sparse Overcomplete Representations, Yee Whye Teh, Max Welling, Simon Osindero, Geoffrey E. Hinton, Journal of Machine Learning Research, vol. 1025-1068, Using very deep autoencoders for content-based image retrieval, Binary coding of speech spectrograms using a deep auto-encoder, Li Deng, Michael L. Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, Geoffrey E. Hinton, Encyclopedia of Machine Learning (2010), pp. 22 (2010), pp. 41 (1993), pp. 1235-1260, Geoffrey E. Hinton, Max Welling, Andriy 8 (1997), pp. Audio, Speech & Language Processing, vol. as a faculty member in the Computer Science department at Carnegie-Mellon University. 2 (1990), pp. Using very deep autoencoders for content-based image retrieval. ///countCtrl.countPageResults("of")/// publications. E. Hinton, Three new graphical models for statistical language modelling, Unsupervised Learning of Image Transformations, Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes, Visualizing Similarity Data with a Mixture of Maps, James Cook, Ilya Sutskever, Andriy Mnih, Geoffrey E. Hinton, A Fast Learning Algorithm for Deep Belief Nets, Geoffrey E. Hinton, Simon Osindero, Yee applications: an overview, Li Deng, Geoffrey E. Hinton, Brian (2012), pp. Dean, G.E. Geoffrey Hinton designs machine learning algorithms. J. Levesque, Learning Sparse Topographic Representations with Products of Student-t 267-277, Simplifying Neural Networks by Soft Weight-Sharing, Neural Computation, vol. 9 (1996), pp. 3 (1991), pp. prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and 68 (1997), pp. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury, Efficient Parametric Projection Pursuit Density Estimation, Max Welling, Richard S. Zemel, Geoffrey E. machines, Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine, George E. Dahl, Marc'Aurelio Ranzato, Abdel-rahman Mohamed, Geoffrey E. Hinton, Phone recognition using Restricted Boltzmann Machines, Rectified Linear Units Improve Restricted Boltzmann Machines, Temporal-Kernel Recurrent Neural Networks, Neural Networks, vol. Geoffrey E. Hinton's Biographical Sketch Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work. 4 (1993), pp. G2R Canada Ranking ... Guide2Research Ranking is based on Google Scholar H-Index. George E. Dahl, Bhuvana Ramabhadran, Geoffrey Neural Networks, vol. Geoffrey Hinton Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google Verified email at cs.toronto.edu Terrance DeVries PhD Candidate, University of Guelph Verified email at uoguelph.ca Matthew Zeiler Founder and CEO, Clarifai Verified email at cs.nyu.edu 969-978, Using fast weights to improve persistent contrastive divergence, Workshop summary: Workshop on learning feature hierarchies, Kai Yu, Ruslan Salakhutdinov, Yann LeCun, Geoffrey E. Hinton, Yoshua Bengio, Zero-shot Learning with Semantic Output Codes, Mark Palatucci, Dean Pomerleau, Geoffrey E. 87 (2012), pp. Welling, Yee Whye Teh, Cognitive Science, vol. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the Killam E. Hinton, Marc Pollefeys, Generating more realistic images using gated MRF's, Marc'Aurelio Ranzato, Volodymyr Mnih, Geoffrey E. Hinton, Learning to Detect Roads in High-Resolution Aerial Images, Learning to Represent Spatial Transformations with Factored Higher-Order Hinton, Jeff Dean, Regularizing Neural Networks by Penalizing M. Neal, Richard S. Zemel, Neural Computation, vol. 23 (2010), pp. google-scholar-export is a Python library for scraping Google scholar profiles to generate a HTML publication lists.. Forum, vol. S. Zemel, Steven L. Small, Stephen C. Strother, Implicit Mixtures of Restricted Boltzmann Machines, Improving a statistical language model by modulating the effects of context words, Zhang Yuecheng, Andriy Mnih, Geoffrey E. 11 (1999), pp. In this Viewpoint, Geoffrey Hinton of Google’s Brain Team discusses the basics of neural networks: their underlying data structures, how they can be trained and combined to process complex health data sets, and future prospects for harnessing their unsupervised learning to clinical challenges. From 2004 until 2013 he was the director of the program on "Neural Computation and Adaptive Perception" which is funded by the Canadian Institute for Advanced Research. at Sussex University and the University of California San Diego and spent five years 838-849, Reinforcement Learning with Factored States and Actions, Journal of Machine Learning Research, vol. Hinton. Communications, vol. All Conferences. Terrence J. Sejnowski, Cognitive Science, vol. Report Missing or Incorrect Information. Their combined citations are counted only for ... Geoffrey Hinton Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google Verified email at cs.toronto.edu. breakthroughs in deep learning that have revolutionized speech recognition and We would like to show you a description here but the site won’t allow us. Convolutional deep belief networks on cifar-10. through online distillation, Rohan Anil, Gabriel Pereyra, Alexandre Tachard Passos, Robert Ormandi, machines, Modeling the joint density of two images under a variety of transformations, Joshua M. Susskind, Geoffrey E. Hinton, Audio, Speech & Language Processing, vol. 2-8, Keeping the Neural Networks Simple by Minimizing the Description Length of the Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Hinton, Tom M. Mitchell, A Scalable Hierarchical Distributed Language Model, Analysis-by-Synthesis by Learning to Invert Generative Black Boxes, Vinod Nair, Joshua M. Susskind, Geoffrey E. He was awarded the first David E. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the Killam prize for Engineering (2012) , The IEEE James Clerk Maxwell Gold medal (2016), and the NSERC Herzberg Gold Medal (2010) which is Canada's top award in Science and Engineering. 3 (1979), pp. Geoffrey Hinton, On Rectified Linear Units For Speech Processing, M.D. Google Scholar; A. Krizhevsky and G.E. Maziarz, Andy Davis, Quoc Le, Geoffrey speech synthesizer controls, IEEE Trans. Hinton, Jacob Goldberger, Sam T. Roweis, Geoffrey E. 132-136, Comparing Classification Methods for Longitudinal fMRI Studies, Tanya Schmah, Grigori Yourganov, Richard S. Zemel, Geoffrey E. Hinton, Steven L. Small, Stephen C. 473-493, Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton, Neural Computation, vol. 8 (1998), pp. Yann LeCun, International Journal of Computer Vision, vol. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. Rectified Linear Units, Quoc V. Le, Navdeep Jaitly, Geoffrey E. Hinton, Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeffrey Koray Kavukcuoglu, Geoffrey E. Hinton, Using Fast Weights to Attend to the Recent Past, Jimmy Ba, Geoffrey Hinton, Volodymyr The ones marked * may be different from the article in the profile. We use the length of the activity vector to represent the probability that the entity exists and Hinton, A New Learning Algorithm for Mean Field Boltzmann Machines, Fiora Pirri, Geoffrey E. Hinton, Hector 37 (1989), pp. Can Improve the Accuracy of Hybrid Models, Navdeep Jaitly, Vincent Vanhoucke, Hinton, Ruslan Salakhutdinov, Probabilistic sequential independent components analysis, IEEE Trans. Since 2013 he has been working half-time Geoffrey Hinton received his Ph.D. degree in Artificial Intelligence from the University of Edinburgh in 1978. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. E. Hinton, Michael A. Picheny, Deep belief nets for natural language call-routing, Ruhi Sarikaya, Geoffrey E. Hinton, 1527-1554, Modeling Human Motion Using Binary Latent Variables, Topographic Product Models Applied to Natural Scene Statistics, Simon Osindero, Max Welling, Geoffrey E. Pattern Anal. has received honorary doctorates from the University of Edinburgh, the University What kind of graphical model is the brain? the Association for the Advancement of Artificial Intelligence. Google Scholar; A. Krizhevsky. Hinton, A Distributed Connectionist Production System, Cognitive Science, vol. Embedding, IEEE Trans. Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Journal of Machine Learning Research, vol. He spent five years as a faculty member at Carnegie Mellon University, Pittsburgh, Pennsylvania, and he is currently a Distinguished Professor at the University of Toronto and a Distinguished Researcher at Google. He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. Unpublished manuscript, 2010. He spent three years Mach. 18 (2005), pp. He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. 13 (2001), pp. Kingsbury, On the importance of initialization and momentum in deep learning, Ilya Sutskever, James Martens, George E. Dahl, Geoffrey E. Hinton, Speech Recognition with Deep Recurrent Neural Networks, Alex Graves, Abdel-rahman Mohamed, Geoffrey Boltzmann Machines, Neural Computation, vol. Hinton, Learning a better representation of speech soundwaves using restricted boltzmann The following articles are merged in Scholar. 1473-1492, Learning to combine foveal glimpses with a third-order Boltzmann machine, Modeling pixel means and covariances using factorized third-order boltzmann 4 (1992), pp. 232-244, Learning Hierarchical Structures with Linear Relational Embedding, Relative Density Nets: A New Way to Combine Backpropagation with HMM's, Extracting Distributed Representations of Concepts and Relations from Positive Hinton, Deep Neural Networks for Acoustic Modeling in Speech Recognition, Geoffrey Hinton, Li Deng, Dong Yu, George 20 (2012), pp. Neural Networks, vol. Does the Wake-sleep Algorithm Produce Good Density Estimators? 889-904, Using Pairs of Data-Points to Define Splits for Decision Trees, An Alternative Model for Mixtures of Experts, Lei Xu 0001, Michael I. Jordan, Geoffrey E. Geoffrey E. Hinton's Biographical Sketch Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. Hinton, Frank Birch, Frank O'Gorman. Peter Dayan, GloveTalkII: An Adaptive Gesture-to-Formant Interface, Peter Dayan, Geoffrey E. Hinton, Radford Sumit Chopra Imagen Technologies ... Y LeCun, Y Bengio, G Hinton. Top 1000 … 113 (2015), pp. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and All Conferences. ///::filterCtrl.getOptionName(optionKey)///, ///::filterCtrl.getOptionCount(filterType, optionKey)///, ///paginationCtrl.getCurrentPage() - 1///, ///paginationCtrl.getCurrentPage() + 1///, ///::searchCtrl.pages.indexOf(page) + 1///. Neural Networks, vol. Report Missing or Incorrect Information. 423-466, GEMINI: Gradient Estimation Through Matrix Inversion After Noise Injection, Yann LeCun, Conrad C. Galland, Geoffrey E. 20 (2012), pp. 120-126, Modeling the manifolds of images of handwritten digits, Geoffrey E. Hinton, Peter Dayan, Michael Bao, Miguel Á. Carreira-Perpiñán, Geoffrey speech recognition, A Better Way to Pretrain Deep Boltzmann Machines, A Practical Guide to Training Restricted Boltzmann Machines, Neural Networks: Tricks of the Trade (2nd ed.) Revow, IEEE Trans. From 2004 until 2013 he was the director of Bhuvana Ramabhadran, Discovering Binary Codes for Documents by Learning Deep Generative Models, Generating Text with Recurrent Neural Networks, Ilya Sutskever, James Martens, Geoffrey E. Terrence J. Sejnowski, A Parallel Computation that Assigns Canonical Object-Based Frames of Reference, Some Demonstrations of the Effects of Structural Descriptions in Mental Imagery, Cognitive Science, vol. 1414-1418, Learning Generative Texture Models with extended Fields-of-Experts, Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton, Modeling pigeon behavior using a Conditional Restricted Boltzmann Machine, Matthew D. Zeiler, Graham W. Taylor, Nikolaus F. Troje, Geoffrey E. Hinton, Replicated Softmax: an Undirected Topic Model, Int. Google Scholar Frosst, Who said what: Modeling individual labelers Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Top 1000 … Mnih, Joel Z. Leibo, Catalin Ionescu, A Simple Way to Initialize Recurrent Networks of Gradient descent can be used for fine-tuning the weights in such “autoencoder” networks, but this works well only if the initial weights are close to a good solution. Try different keywords or filters. He 1078-1101, Discovering Multiple Constraints that are Frequently Approximately Satisfied, Improving deep neural networks for LVCSR using rectified linear units and dropout, George E. Dahl, Tara N. Sainath, Geoffrey E. Hinton, Modeling Documents with Deep Boltzmann Machines, Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey E. Hinton, Marc'Aurelio Ranzato, Volodymyr Mnih, Joshua M. Susskind, Geoffrey E. Hinton, IEEE Trans. Hinton, Neural Computation, vol. 5 (1993), pp. 20 (1987), pp. Pattern Anal. Fleet, Geoffrey E. Hinton, Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images, Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey E. Hinton, Roland Memisevic, Christopher Zach, Geoffrey TIME COVERED 14. He 35 (2013), pp. 38 (2014), pp. 40 (1989), pp. foreign member of the American Academy of Arts and Sciences and the National 15 (2004), pp. 337-346, Recognizing Handwritten Digits Using Hierarchical Products of Experts, IEEE Trans. Processing, Dong Yu, Geoffrey E. Hinton, Nelson 143-150, Dimensionality Reduction and Prior Knowledge in E-Set Recognition, Discovering High Order Features with Mean Field Modules, Phoneme recognition using time-delay neural networks, Alexander H. Waibel, Toshiyuki Hanazawa, Geoffrey E. Hinton, Kiyohiro Shikano, Kevin J. Mach. 193-213, Coaching variables for regression and classification, Statistics and Computing, vol. his PhD in Artificial Intelligence from Edinburgh in 1978. Hinton, Improving neural networks by preventing co-adaptation of feature detectors, Geoffrey E. Hinton, Nitish Srivastava, 355-362, Artif. 46 (1990), pp. He is an honorary foreign member of the American Academy of Arts and Sciences and the National Academy of Engineering, and a former president of the Cognitive Science Society. Their combined citations are counted only for the first article. T. Roweis, Journal of Machine Learning Research, vol. 30 (2006), pp. G2R Canada Ranking ... Guide2Research Ranking is based on Google Scholar H-Index. Geoffrey Everest Hinton CC FRS FRSC (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks.Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.In 2017, he cofounded and became the Chief Scientific Advisor of the Vector Institute in Toronto. Morgan, Jen-Tzung Chien, Shigeki Sagayama, IEEE Trans. 12 (2000), pp. 25-33, Fast Neural Network Emulation of Dynamical Systems for Computer Animation, Radek Grzeszczuk, Demetri Terzopoulos, Geoffrey E. Hinton, Glove-TalkII-a neural-network interface which maps gestures to parallel formant 2109-2128, Split and Merge EM Algorithm for Improving Gaussian Mixture Density Estimates, VLSI Signal Processing, vol. 15 (2014), pp. K. Yang, Q.V. Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. Chorowski, Łukasz Kaiser, Geoffrey Hinton, Who Said What: Modelling Individual Labels Improves He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. 9 (1997), pp. Deoras, IEEE/ACM Trans. His other contributions object classification. 231-250, Aaron Sloman, David Owen, Geoffrey E. now an emeritus distinguished professor. Lang, IEEE Trans. His research group in Toronto made major Merged citations. 50 (2009), pp. Dean, NIPS Deep Learning and Representation Learning Workshop (2015), Oriol Vinyals, Lukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, Geoffrey Hinton, Marc'Aurelio Ranzato, Geoffrey E. Hinton, 65-74, Using Expectation-Maximization for Reinforcement Learning, Neural Computation, vol. J. Approx. 20 (2008), pp. formant speech synthesizer controls, IEEE Trans. images, Tanya Schmah, Geoffrey E. Hinton, Richard Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. Data Eng., vol. Neural Networks, vol. google-scholar-export. 21 (2002), pp. Since 2013 he has been working half-time for Google in Mountain View and Toronto. 22 (2010), pp. He was one of the researchers who introduced the back-propagation algorithm and the first to use backpropagation for learning word embeddings. 702-710, Inferring Motor Programs from Images of Handwritten Digits, Learning Causally Linked Markov Random Fields, Geoffrey E. Hinton, Simon Osindero, Kejie was one of the researchers who introduced the back-propagation algorithm and the Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov, Introduction to the Special Section on Deep Learning for Speech and Language Le, P. Nguyen, A. Hinton, The Recurrent Temporal Restricted Boltzmann Machine, Ilya Sutskever, Geoffrey E. Hinton, for Google in Mountain View and Toronto. 1967-2006, Conditional Restricted Boltzmann Machines for Structured Output Prediction, Volodymyr Mnih, Hugo Larochelle, Geoffrey E. 1385-1403. 275-279, Autoencoders, Minimum Description Length and Helmholtz Free Energy, Developing Population Codes by Minimizing Description Length, Glove-Talk: a neural network interface between a data-glove and a speech Frosst, Geoffrey Hinton, Outrageously Large Neural Networks: The Dayan, A soft decision-directed LMS algorithm for blind equalization, IEEE Trans. His research group in Toronto made major breakthroughs in deep learning that have revolutionized speech recognition and object classification. High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. high-dimensional datasets and to show that this is how the brain learns to see. He was awarded the first David E. 79-87, Adaptive Soft Weight Tying using Gaussian Mixtures, Learning to Make Coherent Predictions in Domains with Discontinuities, A time-delay neural network architecture for isolated word recognition, Kevin J. Lang, Alex Waibel, Geoffrey E. This "Cited by" count includes citations to the following articles in Scholar. Currently, the profile can be scraped from either the Scholar user id, or the Scholar profile URL, resulting in a list of the following: 3 (1990), pp. 14 (2002), pp. Acoustics, Speech, and Signal Processing, vol. Osindero, Local Physical Models for Interactive Character Animation, Comput. Intell., vol. 4 (2003), pp. improves classification, Melody Guan, Varun Geoffrey Hinton received his BA in Experimental Psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. 33-55, A better way to learn features: technical perspective, Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton, Deep Belief Networks using discriminative features for phone recognition, Abdel-rahman Mohamed, Tara N. Sainath, Confident Output Distributions, Gabriel Pereyra, George Tucker, Jan Weights, Learning Mixture Models of Spatial Coherence, Neural Computation, vol. 239-243, 3D Object Recognition with Deep Belief Nets, Factored conditional restricted Boltzmann Machines for modeling motion style, Improving a statistical language model through non-linear prediction, Andriy Mnih, Zhang Yuecheng, Geoffrey E. 381-414, Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation, Geoffrey E. Hinton, Simon Osindero, Max Knowl. He is an honorary 1929-1958, Cognitive Science, vol. Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. Geoffrey E. Hinton Google Brain Toronto {sasabour, frosst, geoffhinton}@google.com Abstract A capsule is a group of neurons whose activity vector represents the instantiation parameters of a specific type of entity such as an object or an object part. Geoffrey Hinton: The Foundations of Deep Learning - YouTube 26 (2000), pp. 47-75, The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm, Neural Computation, vol. ‪Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google‬ - ‪Cited by 397,700‬ - ‪machine learning‬ - ‪psychology‬ - ‪artificial intelligence‬ - ‪cognitive science‬ - ‪computer science‬ Engineering. Geoffrey Hinton designs machine learning algorithms. Gulshan, Andrew M. Dai, Geoffrey Hinton, Attend, Infer, Repeat: Fast Scene Understanding He has received honorary doctorates from the University of Edinburgh, the University of Sussex, and the University of Sherbrooke. Large scale distributed neural network training Yee Whye Teh, Variational Learning in Nonlinear Gaussian Belief Networks, Neural Computation, vol. 831-864, Geoffrey E. Hinton, Zoubin Ghahramani, Hinton, 38th International Conference on Acoustics, Speech and Signal Processing 2629-2636, Generative versus discriminative training of RBMs for classification of fMRI 72 (2009), pp. Hinton, Connectionist Architectures for Artificial Intelligence, IEEE Computer, vol. Strother, Neural Computation, vol. E. Hinton, Speech recognition with deep recurrent neural networks, Yichuan Tang, Ruslan Salakhutdinov, Geoffrey time-delay neural nets, mixtures of experts, variational learning, products of Sparsely-Gated Mixture-of-Experts Layer, Noam Shazeer, Azalia Mirhoseini, Krzysztof Reasoning, vol. DATE OF REPORT (ear, Month, Day) S. PAGE COUNT Technical FROMMar 85 TO Sept 8 September 1985 34 16 SUPPLEMFNTARY NOTATION To be published in J. L. McClelland, D. E. Rumelhart, & the PDP Research Group, 977-984, Hierarchical Non-linear Factor Analysis and Topographic Maps, Instantiating Deformable Models with a Neural Net, Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton, Computer Vision and Image Understanding, vol. Mnih, A Desktop Input Device and Interface for Interactive 3D Character Animation, Sageev Oore, Demetri Terzopoulos, Geoffrey E. 725-731, Improving dimensionality reduction with spectral gradient descent, Neural Networks, vol. 328-339, TRAFFIC: Recognizing Objects Using Hierarchical Reference Frame Transformations, Richard S. Zemel, Michael Mozer, Geoffrey E. Hinton, Glove-TalkII: Mapping Hand Gestures to Speech Using Neural Networks, Recognizing Handwritten Digits Using Mixtures of Linear Models, Geoffrey E. Hinton, Michael Revow, Peter and Negative Propositions, Learning Distributed Representations by Mapping Concepts and Relations into a His aim is to discover a In ESANN, 2011. Hinton, Machine Learning, vol. Intell., vol. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto where he is now an emeritus distinguished professor. Gulshan, Andrew Dai, Geoffrey Hinton, Distilling a Neural Network Into a Soft Decision 4-6, Learning to Label Aerial Images from Noisy Data, Products of Hidden Markov Models: It Takes N>1 to Tango, Robust Boltzmann Machines for recognition and denoising, Understanding how Deep Belief Networks perform acoustic modelling, Abdel-rahman Mohamed, Geoffrey E. Hinton, 12 (1988), pp. Roland Memisevic, Marc Pollefeys, On deep generative models with applications to recognition, Marc'Aurelio Ranzato, Joshua M. Susskind, Volodymyr Mnih, Geoffrey E. Hinton, Geoffrey E. Hinton, Alex Krizhevsky, Sida Hinton. 189-197, Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol. 1771-1800, Global Coordination of Local Linear Models, Sam T. Roweis, Lawrence K. Saul, Geoffrey E. 24 (2002), pp. Classification, Melody Y. Guan, Varun 1 (1989), pp. Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. Hinton, Neurocomputing, vol. Whye Teh, Neural Computation, vol. Distributions, Max Welling, Geoffrey E. Hinton, Simon Hinton, Learning Distributed Representations of Concepts Using Linear Relational 9 (1998), pp. Linear Space, Modeling High-Dimensional Data by Combining Simple Experts, Rate-coded Restricted Boltzmann Machines for Face Recognition, Recognizing Hand-written Digits Using Hierarchical Products of Experts, Naonori Ueda, Ryohei Nakano, Zoubin Ghahramani, Geoffrey E. Hinton, Neural Computation, vol. Exponential Family Harmoniums with an Application to Information Retrieval, Max Welling, Michal Rosen-Zvi, Geoffrey E. (ICASSP), Vancouver (2013), Application of Deep Belief Networks for Natural Language Understanding, Ruhi Sarikaya, Geoffrey E. Hinton, Anoop Tree, Comprehensibility and Explanation in AI and ML (CEX) @ AI*IA 2017 (2017), Sara Sabour, Nicholas ‪Emeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, Google‬ - ‪Cited by 397,700‬ - ‪machine learning‬ - ‪psychology‬ - ‪artificial intelligence‬ - ‪cognitive science‬ - ‪computer science‬ Senior, V. Vanhoucke, J. 778-784, Dropout: a simple way to prevent neural networks from overfitting, Nitish Srivastava, Geoffrey E. Hinton, 599-619, Acoustic Modeling Using Deep Belief Networks, Abdel-rahman Mohamed, George E. Dahl, Geoffrey E. Hinton, IEEE Trans. learning procedure that is efficient at finding complex structure in large, 9 (1985), pp. 18 (2006), pp. Geoffrey Everest Hinton CC FRS FRSC (born 6 December 1947) is an English Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks.Since 2013 he divides his time working for Google (Google Brain) and the University of Toronto.In 2017, he cofounded and became the Chief Scientific Advisor of the Vector Institute in Toronto. Top Conferences. No results found. 24 (2012), pp. the NSERC Herzberg Gold Medal (2010) which is Canada's top award in Science and Geoffrey Hinton University of Toronto Canada: G2R World Ranking 13th. 185-234, Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space, Neural Computation, vol. 23-43, Building adaptive interfaces with neural networks: The glove-talk pilot study, Connectionist Symbol Processing - Preface, Discovering Viewpoint-Invariant Relationships That Characterize Objects, Evaluation of Adaptive Mixtures of Competing Experts, Mapping Part-Whole Hierarchies into Connectionist Networks, Artif. Intell., vol. 147-169, Shape Recognition and Illusory Conjunctions, Symbols Among the Neurons: Details of a Connectionist Inference Architecture, Massively Parallel Architectures for AI: NETL, Thistle, and Boltzmann Machines, Scott E. Fahlman, Geoffrey E. Hinton, Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and Graph. He then became a fellow of the Canadian Institute for Advanced Research and moved to 18 (2006), pp. Hinton, Deep, Narrow Sigmoid Belief Networks Are Universal Approximators, Neural Computation, vol. nature 521 (7553), 436-444, 2015. Since 2013 he has been working half-time for Google in Mountain View and Toronto. Top Conferences. Canadian Institute for Advanced Research. Godfather of artificial intelligence Geoffrey Hinton gives an overview of the foundations of deep learning. University College London and then returned to the University of Toronto where he is 7 (1995), pp. 22 (2014), pp. with Generative Models, S. M. Ali Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, 9 (1997), pp. 1-2, Autoregressive Product of Multi-frame Predictions Task, Variational Learning for Switching State-Space Models, Neural Computation, vol. experts and deep belief nets. Dudek, Neural Computation, vol. He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. Intell., vol. first to use backpropagation for learning word embeddings. 100-109, Learning Representations by Recirculation, Learning Translation Invariant Recognition in Massively Parallel Networks, Learning in Massively Parallel Nets (Panel), A Learning Algorithm for Boltzmann Machines, David H. Ackley, Geoffrey E. Hinton, 267-269, Dynamical binary latent variable models for 3D human pose tracking, Graham W. Taylor, Leonid Sigal, David J. Audio, Speech & Language Processing, vol. 271-278, Data Compression Conference (1996), pp. to neural network research include Boltzmann machines, distributed representations, TYPE OF REPORT 13b. 73-81, Neural Networks, vol. 205-212, NeuroAnimator: Fast Neural Network Emulation and Control of Physics-based Models, Sageev Oore, Geoffrey E. Hinton, Gregory Graham W. Taylor, Using matrices to model symbolic relationship, Learning Multilevel Distributed Representations for High-Dimensional Sequences, Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure, Modeling image patches with a directed hierarchy of Markov random fields, Restricted Boltzmann machines for collaborative filtering, Ruslan Salakhutdinov, Andriy Mnih, Geoffrey 12 (2011), pp. the Department of Computer Science at the University of Toronto. Gerald Penn, Visualizing non-metric similarities in multiple maps, Laurens van der Maaten, Geoffrey E. 133-140, Using Free Energies to Represent Q-values in a Multiagent Reinforcement Learning from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at Hinton, Neural Networks, vol. The following articles are merged in Scholar. 683-699, Efficient Stochastic Source Coding and an Application to a Bayesian Network David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams 13a. 5 (2004), pp. Brendan J. Frey, Geoffrey E. Hinton, D. Wang, Two Distributed-State Models For Generating High-Dimensional Time Series, Graham W. Taylor, Geoffrey E. Hinton, Sam Neural Networks, vol. His aim is to discover a learning procedure that is efficient at finding complex structure in large, high-dimensional datasets and to show that this is how the brain learns to see. Source Model, Glove-talk II - a neural-network interface which maps gestures to parallel 12 (2000), pp. E. Hinton, Using an autoencoder with deformable templates to discover features for automated 2729-2762, Encyclopedia of Machine Learning (2010), pp. Add co-authors Co-authors. 2206-2222, New types of deep neural network learning for speech recognition and related the program on "Neural Computation and Adaptive Perception" which is funded by the Zeiler, M. Ranzato, R. Monga, M. Mao, Academy of Engineering, and a former president of the Cognitive Science Society. of Sussex, and the University of Sherbrooke. George Dahl, Geoffrey Hinton, Geoffrey Hinton, Sara Sabour, Nicholas synthesizer, IEEE Trans. 8 (1997), pp.